Item Familiarity as a Possible Confounding Factor in User-Centric Recommender Systems Evaluation

i-com ◽  
2015 ◽  
Vol 14 (1) ◽  
pp. 29-39 ◽  
Author(s):  
Dietmar Jannach ◽  
Lukas Lerche ◽  
Michael Jugovac
Keyword(s):  
Recommender Systems ◽  
User Satisfaction ◽  
User Study ◽  
Predictive Accuracy ◽  
Confounding Factor ◽  
Academic Research ◽  
Strongly Correlated ◽  
Quality Factors ◽  
User Centric ◽  
Assess Quality

AbstractUser studies play an important role in academic research in the field of recommender systems as they allow us to assess quality factors other than the predictive accuracy of the underlying algorithms. User satisfaction is one such factor that is often evaluated in laboratory settings and in many experimental designs one task of the participants is to assess the suitability of the system-generated recommendations. The effort required by the user to make such an assessment can, however, depend on the user’s familiarity with the presented items and directly impact on the reported user satisfaction. In this paper, we report the results of a preliminary recommender systems user study using Mechanical Turk, which indicates that item familiarity is strongly correlated with overall satisfaction.

A Characterisation and Framework for User-Centric Factors in Evaluation Methods for Recommender Systems

2017 ◽  
Vol 6 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Tatenda D. Kavu ◽  
Kuda Dube ◽  
Peter G. Raeth ◽  
Gilford T. Hapanyengwi
Keyword(s):  
Conceptual Framework ◽  
Recommender Systems ◽  
User Experience ◽  
User Satisfaction ◽  
Optimal Solution ◽  
User Interaction ◽  
Evaluation Methods ◽  
User Preferences ◽  
Evaluation Framework ◽  
User Centric

Researchers have worked on-finding e-commerce recommender systems evaluation methods that contribute to an optimal solution. However, existing evaluations methods lack the assessment of user-centric factors such as buying decisions, user experience and user interactions resulting in less than optimum recommender systems. This paper investigates the problem of adequacy of recommender systems evaluation methods in relation to user-centric factors. Published work has revealed limitations of existing evaluation methods in terms of evaluating user satisfaction. This paper characterizes user-centric evaluation factors and then propose a user-centric evaluation conceptual framework to identify and expose a gap within literature. The researchers used an integrative review approach to formulate both the characterization and the conceptual framework for investigation. The results reveal a need to come up with a holistic evaluation framework that combines system-centric and user-centric evaluation methods as well as formulating computational user-centric evaluation methods. The conclusion reached is that, evaluation methods for e-commerce recommender systems lack full assessment of vital factors such as: user interaction, user experience and purchase decisions. A full consideration of these factors during evaluation will give birth to new types of recommender systems that predict user preferences using user decision-making process profiles, and that will enhance user experience and increase revenue in the long run.

scholarly journals Exploiting a Map-Based Interface in Conversational Recommender Systems for Mobile Travelers

2010 ◽  
pp. 73-93 ◽  
Author(s):  
Francesco Ricci ◽  
Quang Nhat Nguyen ◽  
Olga Averjanova
Keyword(s):  
Recommender Systems ◽  
Web Sites ◽  
User Satisfaction ◽  
User Study ◽  
Information Overload ◽  
Mobile Internet ◽  
Amount Of Information ◽  
Travel And Tourism ◽  
Related Information ◽  
Usage Context

Nowadays travel and tourism Web sites store and offer a large volume of travel related information and services. Furthermore, this huge amount of information can be easily accessed using mobile devices, such as a phone with mobile Internet connection capability. However, this information can easily overwhelm a user because of the large number of information items to be shown and the limited screen size in the mobile device. Recommender systems (RSs) are often used in conjunction with Web tools to effectively help users in accessing this overwhelming amount of information. These recommender systems can support the user in making a decision even when specific knowledge necessary to autonomously evaluate the offerings is not available. Recommender systems cope with the information overload problem by providing a user with personalized recommendations (i.e., a well chosen selection of the items contained in the repository), adapting this selection to the user’s needs and preferences in a particular usage context. In this chapter, the authors present a recommendation approach integrating a conversational preference acquisition technology based on “critiquing” with map visualization technologies to build a new map-based conversational mobile RS that can effectively and intuitively support travelers in finding their desired products and services. The results of the authors’ real-user study show that integrating map-based visualization and critiquing-based interaction in mobile RSs improves the system’s recommendation effectiveness, and increases the user satisfaction.

Correlation Between Gestures’ Qualitative Properties and Usa- bility metrics

2020 ◽  
Vol 64 (1) ◽  
pp. 726-730
Author(s):  
Daniela Chanci ◽  
Naveen Madapana ◽  
Glebys Gonzalez ◽  
Juan Wachs
Keyword(s):  
Linear Regression ◽  
User Satisfaction ◽  
Completion Time ◽  
User Study ◽  
Task Completion ◽  
Time Error ◽  
Qualitative Properties ◽  
Regression Techniques ◽  
Overall Efficiency ◽  
Usability Metrics

The choice of best gestures and commands for touchless interfaces is a critical step that determines the user- satisfaction and overall efficiency of surgeon computer interaction. In this regard, usability metrics such as task completion time, error rate, and memorability have a long-standing as potential entities in determining the best gesture vocabulary. In addition, some previous works concerned with this problem have utilized qualitative measures to identify the best gesture. In this work, we hypothesize that there is a correlation between the qualitative properties of gestures (v) and their usability metrics (u). Therefore, we conducted an experiment with linguists to quantify the properties of the gestures. Next, a user study was conducted with surgeons, and the usability metrics were measured. Lastly, linear and non-linear regression techniques were used to find the correlations between u and v. Results show that usability metrics are correlated with the gestures’ qualitative properties ( R2 = 0.4).

scholarly journals Low-Asymmetry Interface for Multiuser VR Experiences with Both HMD and Non-HMD Users

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 397
Author(s):  
Qimeng Zhang ◽  
Ji-Su Ban ◽  
Mingyu Kim ◽  
Hae Won Byun ◽  
Chang-Hun Kim
Keyword(s):  
Virtual Reality ◽  
Virtual Environment ◽  
Mobile Device ◽  
User Satisfaction ◽  
Virtual World ◽  
User Study ◽  
Visual Display ◽  
Point Of View ◽  
Head Mounted Display ◽  
And Behavior

We propose a low-asymmetry interface to improve the presence of non-head-mounted-display (non-HMD) users in shared virtual reality (VR) experiences with HMD users. The low-asymmetry interface ensures that the HMD and non-HMD users’ perception of the VR environment is almost similar. That is, the point-of-view asymmetry and behavior asymmetry between HMD and non-HMD users are reduced. Our system comprises a portable mobile device as a visual display to provide a changing PoV for the non-HMD user and a walking simulator as an in-place walking detection sensor to enable the same level of realistic and unrestricted physical-walking-based locomotion for all users. Because this allows non-HMD users to experience the same level of visualization and free movement as HMD users, both of them can engage as the main actors in movement scenarios. Our user study revealed that the low-asymmetry interface enables non-HMD users to feel a presence similar to that of the HMD users when performing equivalent locomotion tasks in a virtual environment. Furthermore, our system can enable one HMD user and multiple non-HMD users to participate together in a virtual world; moreover, our experiments show that the non-HMD user satisfaction increases with the number of non-HMD participants owing to increased presence and enjoyment.

scholarly journals Autonomous Motivation and Self-Disclosure Intention

2021 ◽  
Vol 33 (6) ◽  
pp. 1-27
Author(s):  
Wei-Tsong Wang ◽  
Wei-Ming Ou ◽  
Wei-Chi Chiu
Keyword(s):  
User Satisfaction ◽  
Autonomous Motivation ◽  
Self Determination Theory ◽  
Self Determination ◽  
Quality Factors ◽  
User Intention ◽  
Self Disclosure ◽  
Research Results ◽  
Related Information ◽  
Related Quality

Studies that specifically discuss the formation of autonomous motivations of users of social networking services (SNSs) and how such motivation influences SNS user intention to disclose personal location-related information (PLRI) are absent from the literature. Consequently, this study, based on the self-determination theory and the information system success (ISS) model, investigates the relationships among key system-related quality factors, SNS users’ autonomous motivations and user satisfaction regarding an SNS, and their intentions to disclose PLRI. Survey data collected from 514 students at six universities were analyzed to validate our research model. Research results show that three system-related quality factors have different influences on user satisfaction and autonomous motivation, while both autonomous motivation and user satisfaction are significant antecedents of user intention to disclose PLRI. The research results have extended the application and advanced the understanding of ISS model and self-determination theory in the context of SNS.

scholarly journals Symmetric Metric Learning with Adaptive Margin for Recommendation

2020 ◽  
Vol 34 (04) ◽  
pp. 4634-4641
Author(s):  
Mingming Li ◽  
Shuai Zhang ◽  
Fuqing Zhu ◽  
Wanhui Qian ◽  
Liangjun Zang ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Metric Space ◽  
State Of The Art ◽  
Metric Learning ◽  
Current User ◽  
Experimental Results ◽  
Negative Item ◽  
Positive Item ◽  
User Centric ◽  
The Impact

Metric learning based methods have attracted extensive interests in recommender systems. Current methods take the user-centric way in metric space to ensure the distance between user and negative item to be larger than that between the current user and positive item by a fixed margin. While they ignore the relations among positive item and negative item. As a result, these two items might be positioned closely, leading to incorrect results. Meanwhile, different users usually have different preferences, the fixed margin used in those methods can not be adaptive to various user biases, and thus decreases the performance as well. To address these two problems, a novel Symmetic Metric Learning with adaptive margin (SML) is proposed. In addition to the current user-centric metric, it symmetically introduces a positive item-centric metric which maintains closer distance from positive items to user, and push the negative items away from the positive items at the same time. Moreover, the dynamically adaptive margins are well trained to mitigate the impact of bias. Experimental results on three public recommendation datasets demonstrate that SML produces a competitive performance compared with several state-of-the-art methods.

Transcriptome Analysis of lncRNAs in Pheochromocytomas and Paragangliomas

2019 ◽  
Vol 105 (3) ◽  
pp. 898-907 ◽  
Author(s):  
Sylvie Job ◽  
Adrien Georges ◽  
Nelly Burnichon ◽  
Alexandre Buffet ◽  
Laurence Amar ◽  
...  
Keyword(s):  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Rank Test ◽  
Benign Tumors ◽  
Strongly Correlated ◽  
Metastatic Progression ◽  
Roc Curve Analysis ◽  
Chi Square ◽  
Consensus Analysis ◽  
Design And Methods

Abstract Context Pheochromocytomas and paragangliomas (PPGLs) are neuroendocrine tumors explained by germline or somatic mutations in about 70% of cases. Patients with SDHB mutations are at high risk of developing metastatic disease, yet no reliable tumor biomarkers are available to predict tumor aggressiveness. Objective We aimed at identifying long noncoding RNAs (lncRNAs) specific for PPGL molecular groups and metastatic progression. Design and Methods To analyze the expression of lncRNAs, we used a mining approach of transcriptome data from a well-characterized series of 187 tumor tissues. Clustering consensus analysis was performed to determine a lncRNA-based classification, and informative transcripts were validated in an independent series of 51 PPGLs. The expression of metastasis-related lncRNAs was confirmed by RT-qPCR. Receiver operating characteristic (ROC) curve analysis was used to estimate the predictive accuracy of potential markers. Main Outcome Measure Univariate/multivariate and metastasis-free survival (MFS) analyses were carried out for the assessment of risk factors and clinical outcomes. Results Four lncRNA-based subtypes strongly correlated with mRNA expression clusters (chi-square P-values from 1.38 × 10–32 to 1.07 × 10–67). We identified one putative lncRNA (GenBank: BC063866) that accurately discriminates metastatic from benign tumors in patients with SDHx mutations (area under the curve 0.95; P = 4.59 × 10–05). Moreover, this transcript appeared as an independent risk factor associated with poor clinical outcome of SDHx carriers (log-rank test P = 2.29 × 10–05). Conclusion Our findings extend the spectrum of transcriptional dysregulations in PPGL to lncRNAs and provide a novel biomarker that could be useful to identify potentially metastatic tumors in patients carrying SDHx mutations.

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